Notes and Conventions

Notes

This is an example R Markdown report to demonstrate the workflow for building a “data memo” or report object in parallel with the conduct of a RsNLME analysis.
We use html output and “tabsets” within the RMarkdown document to show how RsNLME can be used together with R markdown to produce very dense, interactive data summaries that capture a large amount of data in well organized, easily shared delivery mechanism.

Conventions used in this document

We’ve used blue text boxes to indicate descriptive information

We’ve used yellow text boxes to indicate results

Green text boxes provide key messages from the analysis results
Example: This is a key message statement

# Analysis ID     : Certara.R School Lesson 4 - Modeling with RsNLME
# Purpose         : Introduce R Markdown 
# Other Info      : 
library(flextable)
library(plotly)

Objectives

The purpose of this data memo is to provide a summary of the base population pharmacokinetic model fit for study XYZ

Study Design

150 healthy male and female subjects were enrolled in this study
Subjects were administered a single 5000, 10000, or 20000 ug dose of Drug XYZ at time=0
Blood samples were obtained at 0, 0.25, 0.5, 1, 2, 3, 4, 6, 8, 12, 16 and 24 hours after dosing
Covariates in the dataset include baseline subject age, weight, sex, and race

Methods

Data analysis was conducted using the population pharmacokinetic program RsNLME version 1.1.0 (https://certara.github.io/R-RsNLME/index.html)
etc…..

Overview of the data

Summary

Elimination profile appears to be mono-exponential
Body weight and/or gender appear to influence PK

Demography summary

dm_table <- readRDS(file='../Lesson_4/dmtable.RDS')
dm_table

Characteristic	N = 150¹
AGE	50.5 (38.0 - 61.0)
WT	71 (55 - 94)
SEX
F	45 (30%)
M	105 (70%)
RACE
ASIAN	21 (14%)
BLACK	36 (24%)
OTHER	33 (22%)
WHITE	60 (40%)
¹ Mean (Range); n (%)

Mean Concentration Summary

mean_conc_table <- readRDS(file='../Lesson_4/mean_conc_table.RDS')
mean_conc_table

Mean Concentration by Dose Group and Time
	Dose Group
0.00	0 (0-0)	0 (0-0)	0 (0-0)
0.25	19.5 (5.19-56.4)	38.8 (4.77-111)	58.3 (6.83-164)
0.50	32.6 (9.88-84.3)	64.9 (19-167)	96.9 (27.9-247)
1.00	48.1 (17.2-119)	96.3 (30.6-235)	144 (41.1-349)
2.00	56.2 (17.7-116)	112 (32.7-236)	169 (51.7-352)
3.00	55.8 (19.8-118)	112 (37.7-235)	167 (56-348)
4.00	51.7 (15.4-131)	103 (27.5-264)	155 (37.8-400)
6.00	41.2 (9.77-91.4)	82.4 (14.3-182)	124 (21.9-270)
8.00	34.7 (9.36-67)	69.9 (21.7-136)	104 (29.8-199)
12.00	25 (2.98-69.8)	49.6 (5.06-140)	75.8 (8.52-208)
16.00	18.7 (1.08-57.5)	37.5 (0-115)	56.8 (0-169)
24.00	9.52 (0.103-32.9)	19.3 (0-67.3)	28.1 (0-99.8)

PK profiles Linear

pkplot <- readRDS(file='../Lesson_4/pkplot.RDS')

ggplotly(pkplot)

PK profiles Log

Disposition appears mono-exponential

pkplotlog <- readRDS(file='../Lesson_4/pkplotlog.RDS')
pkplotlog

Facet Gender and Dose Linear

Possible gender difference in PK with observed concentrations higher in females

pkplot_dosesex <- readRDS(file='../Lesson_4/pkplot_dosesex.RDS')
pkplot_dosesex

Facet Race and Dose Linear

PK appears similar across race

pkplot_doserace <- readRDS(file='../Lesson_4/pkplot_doserace.RDS')
pkplot_doserace

Facet WT Linear

Possible WT influence on PK with larger subjects having lower exposures

pkplot_dosewt <- readRDS(file='../Lesson_4/pkplot_dosewt.RDS')
pkplot_dosewt

Facet Age Linear

PK appears similar across age groups. Note limited age range (38 to 61 y).

pkplot_doseage <- readRDS(file='../Lesson_4/pkplot_doseage.RDS')
pkplot_doseage

Base Model Development

Summary

A 1 compartment model with first order oral input and elimination was used to fit the data
Convergence was achieved with successful estimation of parameter standard errors

Model Description

A 1 compartment model with first order oral input and elimination was used to fit the data
Model parameters absorption rate constant (Ka), central clearance (Cl), and central volume (V)
Between subject variability terms on Ka, Cl and V
Additive residual error model

Structural PK Model

## View the updated model 
basemod<-readRDS("../Lesson_5/basemod.RDS")
print(basemod)

## Loading required package: Certara.RsNLME

## 
##  Model Overview 
##  ------------------------------------------- 
## Is population     :  TRUE
## Model Type        :  PK
## 
##  PK 
##  ------------------------------------------- 
## Parameterization  :  Clearance
## Absorption        :  FirstOrder
## Num Compartments  :  1
## Dose Tlag?        :  FALSE
## Elimination Comp ?:  FALSE
## Infusion Allowed ?:  FALSE
## Sequential        :  FALSE
## Freeze PK         :  FALSE
## 
##  PML 
##  ------------------------------------------- 
## test(){
##     cfMicro(A1,Cl/V, first = (Aa = Ka))
##     dosepoint(Aa)
##     C = A1 / V
##     error(CEps=5)
##     observe(CObs=C + CEps)
##     stparm(Ka = tvKa * exp(nKa))
##     stparm(V = tvV * exp(nV))
##     stparm(Cl = tvCl * exp(nCl))
##     fcovariate(AGE)
##     fcovariate(WT)
##     fcovariate(SEX())
##     fcovariate(RACE())
##     fcovariate(DOSEGRP)
##     fixef( tvKa = c(,1.5,))
##     fixef( tvV = c(,80,))
##     fixef( tvCl = c(,9,))
##     ranef(diag(nKa,nV,nCl) = c(0.1,0.1,0.1))
## }
## 
##  Structural Parameters 
##  ------------------------------------------- 
##  Ka V Cl
##  ------------------------------------------- 
## Observations:
## Observation Name :  CObs
## Effect Name      :  C
## Epsilon Name     :  CEps
## Epsilon Type     :  Additive
## Epsilon frozen   :  FALSE
## is BQL           :  FALSE
##  ------------------------------------------- 
##  Column Mappings 
##  ------------------------------------------- 
## Model Variable Name : Data Column name
## id                  : ID
## time                : TIME
## Aa                  : AMT
## AGE                 : AGE
## WT                  : WT
## SEX                 : SEX( M=0 F=1 )
## RACE                : RACE( WHITE=0 BLACK=1 ASIAN=2 OTHER=3 )
## DOSEGRP             : DOSEGRP
## CObs                : CONC

Model Fit Summary

Convergence was achieved with successful estimation of parameter standard errors

Additive Error Model

## View the updated model 
basemodfit<-readRDS("../Lesson_5/basemodfit.RDS")
print(basemodfit)

## $Overall
##    Scenario RetCode    LogLik     -2LL      AIC      BIC nParm nObs nSub
## 1: WorkFlow       1 -7187.912 14375.82 14389.82 14427.68     7 1650  150
##    EpsShrinkage Condition
## 1:      0.10699  73.06982
## 
## $theta
##    #Scenario Parameter  Estimate Units     Stderr      CV%    2.5%CI   97.5%CI
## 1:  WorkFlow      tvKa  1.195375    NA 0.03937092 3.293605  1.118152  1.272597
## 2:  WorkFlow       tvV 80.425584    NA 2.83247767 3.521861 74.869937 85.981231
## 3:  WorkFlow      tvCl  7.731636    NA 0.42486324 5.495127  6.898305  8.564967
## 4:  WorkFlow      CEps 13.789523    NA 0.77473096 5.618258 12.269959 15.309088
##    Var.Inf.factor
## 1:             NA
## 2:             NA
## 3:             NA
## 4:             NA
## 
## $omega
##    Label        nKa        nV       nCl
## 1:   nKa 0.07185426 0.0000000 0.0000000
## 2:    nV 0.00000000 0.1687598 0.0000000
## 3:   nCl 0.00000000 0.0000000 0.3953833
## 
## $omega_Correlation
##    Label nKa nV nCl
## 1:   nKa   1  0   0
## 2:    nV   0  1   0
## 3:   nCl   0  0   1
## 
## $Eta_Shrinkage
##        Label       nKa         nV        nCl
## 1: Shrinkage 0.3216746 0.05676357 0.06095907
## 
## $omega_stderr
##    Label        nKa         nV        nCl
## 1:   nKa 0.01546955 0.00000000 0.00000000
## 2:    nV 0.00000000 0.02327878 0.00000000
## 3:   nCl 0.00000000 0.00000000 0.04707696

Multiplicative Error Model

## View the updated model 
basemodNewfit<-readRDS("../Lesson_6/basemodNewfit.RDS")
print(basemodNewfit)

## $Overall
##    Scenario RetCode    LogLik     -2LL      AIC   BIC nParm nObs nSub
## 1: WorkFlow       1 -6744.071 13488.14 13502.14 13540     7 1650  150
##    EpsShrinkage Condition
## 1:      0.12191  87.19756
## 
## $theta
##    #Scenario Parameter   Estimate Units      Stderr      CV%     2.5%CI
## 1:  WorkFlow      tvKa  1.1754929    NA 0.034656912 2.948288  1.1075165
## 2:  WorkFlow       tvV 81.3292974    NA 2.958785126 3.638031 75.5259100
## 3:  WorkFlow      tvCl  7.9344104    NA 0.413967465 5.217369  7.1224510
## 4:  WorkFlow      CEps  0.2066483    NA 0.009880772 4.781443  0.1872681
##       97.5%CI Var.Inf.factor
## 1:  1.2434693             NA
## 2: 87.1326849             NA
## 3:  8.7463699             NA
## 4:  0.2260286             NA
## 
## $omega
##    Label        nKa        nV       nCl
## 1:   nKa 0.07954013 0.0000000 0.0000000
## 2:    nV 0.00000000 0.1782003 0.0000000
## 3:   nCl 0.00000000 0.0000000 0.3700297
## 
## $omega_Correlation
##    Label nKa nV nCl
## 1:   nKa   1  0   0
## 2:    nV   0  1   0
## 3:   nCl   0  0   1
## 
## $Eta_Shrinkage
##        Label       nKa         nV        nCl
## 1: Shrinkage 0.2154865 0.03304809 0.02180766
## 
## $omega_stderr
##    Label        nKa         nV        nCl
## 1:   nKa 0.01541487 0.00000000 0.00000000
## 2:    nV 0.00000000 0.02314708 0.00000000
## 3:   nCl 0.00000000 0.00000000 0.03886903

Base Model Diagnostics

Additive Residual Error Model

Structural Model

A multiplicative residual error model was selected in place of
the base model additive error model

DV vs PRED

basedvpred <- readRDS(file='../Lesson_6/basedvpred.RDS')
basedvpred

DV vs IPRED

basedvipred <- readRDS(file='../Lesson_6/basedvipred.RDS')
basedvipred

CWRES vs TIME

baseresidv <- readRDS(file='../Lesson_6/baseresidv.RDS')
baseresidv

CWRES vs IPRED

baserespred <- readRDS(file='../Lesson_6/baserespred.RDS')
baserespred

absCWRES vs IPRED

basearespred <- readRDS(file='../Lesson_6/basearespred.RDS')
basearespred

Covariate Model

CL appears to be influenced by subject bodyweight

Parameter vs WT

baseprmcovwt <- readRDS(file='../Lesson_6/baseprmcovwt.RDS')
baseprmcovwt

ETA vs WT

baseetacovwt <- readRDS(file='../Lesson_6/baseetacovwt.RDS')
baseetacovwt

Multiplicative Residual Error Model

Structural Model

A multiplicative residual error model was selected in place of
the base model additive error model

DV vs PRED

basenewdvpred <- readRDS(file='../Lesson_6/basenewdvpred.RDS')
basenewdvpred

DV vs IPRED

basenewdvipred <- readRDS(file='../Lesson_6/basenewdvipred.RDS')
basenewdvipred

CWRES vs TIME

basenewresidv <- readRDS(file='../Lesson_6/basenewresidv.RDS')
basenewresidv

CWRES vs IPRED

basenewrespred <- readRDS(file='../Lesson_6/basenewrespred.RDS')
basenewrespred

absCWRES vs IPRED

basenewarespred <- readRDS(file='../Lesson_6/basenewarespred.RDS')
basenewarespred

Covariate Model

CL appears to be influenced by subject bodyweight

Parameter vs WT

basenewprmcovwt <- readRDS(file='../Lesson_6/basenewprmcovwt.RDS')
basenewprmcovwt

ETA vs WT

basenewetacovwt <- readRDS(file='../Lesson_6/basenewetacovwt.RDS')
basenewetacovwt

ETAs vs All Covariates

basenewranparcov <- readRDS(file='../Lesson_6/basenewranparcov.RDS')
basenewranparcov

## [[1]]

Covariate Models

Summary

Significant covariates in single covariate model runs include…

Run1WTonCL

Model Fit Summary

run1WTonCLfit<-readRDS("../Lesson_6/run1WTonCLfit.RDS")
print(run1WTonCLfit)

## $Overall
##    Scenario RetCode    LogLik     -2LL      AIC      BIC nParm nObs nSub
## 1: WorkFlow       1 -6717.733 13435.47 13451.47 13494.74     8 1650  150
##    EpsShrinkage Condition
## 1:      0.12181  87.91124
## 
## $theta
##    #Scenario Parameter   Estimate Units      Stderr       CV%     2.5%CI
## 1:  WorkFlow      tvKa  1.1786065    NA 0.034745861  2.948046  1.1104557
## 2:  WorkFlow       tvV 81.6550756    NA 2.989533797  3.661173 75.7913748
## 3:  WorkFlow      tvCl  7.7782270    NA 0.345639551  4.443680  7.1002862
## 4:  WorkFlow    dCldWT  2.7880019    NA 0.337859458 12.118337  2.1253211
## 5:  WorkFlow      CEps  0.2067064    NA 0.009895362  4.787158  0.1872975
##       97.5%CI Var.Inf.factor
## 1:  1.2467574             NA
## 2: 87.5187764             NA
## 3:  8.4561678             NA
## 4:  3.4506828             NA
## 5:  0.2261153             NA
## 
## $omega
##    Label        nKa       nV      nCl
## 1:   nKa 0.07919197 0.000000 0.000000
## 2:    nV 0.00000000 0.179176 0.000000
## 3:   nCl 0.00000000 0.000000 0.259979
## 
## $omega_Correlation
##    Label nKa nV nCl
## 1:   nKa   1  0   0
## 2:    nV   0  1   0
## 3:   nCl   0  0   1
## 
## $Eta_Shrinkage
##        Label       nKa        nV        nCl
## 1: Shrinkage 0.2176036 0.0361539 0.02312065
## 
## $omega_stderr
##    Label        nKa         nV        nCl
## 1:   nKa 0.01552033 0.00000000 0.00000000
## 2:    nV 0.00000000 0.02348581 0.00000000
## 3:   nCl 0.00000000 0.00000000 0.02618201

Structural Model

DV vs PRED

run1dvpred <- readRDS(file='../Lesson_6/run1dvpred.RDS')
run1dvpred

DV vs IPRED

run1dvipred <- readRDS(file='../Lesson_6/run1dvipred.RDS')
run1dvipred

CWRES vs TIME

run1residv <- readRDS(file='../Lesson_6/run1residv.RDS')
run1residv

CWRES vs IPRED

run1respred <- readRDS(file='../Lesson_6/run1respred.RDS')
run1respred

Covariate Model

ETAs vs All Covariates

run1ranparcov <- readRDS(file='../Lesson_6/run1ranparcov.RDS')
run1ranparcov

## [[1]]

Run2SEXonCL

Model Fit Summary

run2SEXonCLfit<-readRDS("../Lesson_6/run2SEXonCLfit.RDS")
print(run2SEXonCLfit)

## $Overall
##    Scenario RetCode    LogLik     -2LL      AIC      BIC nParm nObs nSub
## 1: WorkFlow       1 -6742.824 13485.65 13501.65 13544.92     8 1650  150
##    EpsShrinkage Condition
## 1:      0.12214  88.17658
## 
## $theta
##    #Scenario Parameter   Estimate Units      Stderr        CV%     2.5%CI
## 1:  WorkFlow      tvKa  1.1760957    NA 0.034680123   2.948750  1.1080738
## 2:  WorkFlow       tvV 81.5210067    NA 2.974464460   3.648709 75.6868631
## 3:  WorkFlow      tvCl  8.3451700    NA 0.515055134   6.171895  7.3349359
## 4:  WorkFlow  dCldSEX1 -0.1753534    NA 0.123366998 -70.353348 -0.4173266
## 5:  WorkFlow      CEps  0.2066220    NA 0.009864526   4.774190  0.1872736
##        97.5%CI Var.Inf.factor
## 1:  1.24411767             NA
## 2: 87.35515040             NA
## 3:  9.35540422             NA
## 4:  0.06661982             NA
## 5:  0.22597038             NA
## 
## $omega
##    Label        nKa        nV       nCl
## 1:   nKa 0.08075958 0.0000000 0.0000000
## 2:    nV 0.00000000 0.1787324 0.0000000
## 3:   nCl 0.00000000 0.0000000 0.3648156
## 
## $omega_Correlation
##    Label nKa nV nCl
## 1:   nKa   1  0   0
## 2:    nV   0  1   0
## 3:   nCl   0  0   1
## 
## $Eta_Shrinkage
##        Label       nKa        nV        nCl
## 1: Shrinkage 0.2172781 0.0340716 0.02289959
## 
## $omega_stderr
##    Label        nKa         nV       nCl
## 1:   nKa 0.01527397 0.00000000 0.0000000
## 2:    nV 0.00000000 0.02297015 0.0000000
## 3:   nCl 0.00000000 0.00000000 0.0377524

Structural Model

DV vs PRED

run2dvpred <- readRDS(file='../Lesson_6/run2dvpred.RDS')
run2dvpred

DV vs IPRED

run2dvipred <- readRDS(file='../Lesson_6/run2dvipred.RDS')
run2dvipred

CWRES vs TIME

run2residv <- readRDS(file='../Lesson_6/run2residv.RDS')
run2residv

CWRES vs IPRED

run2respred <- readRDS(file='../Lesson_6/run2respred.RDS')
run2respred

Covariate Model

ETAs vs All Covariates

run2ranparcov <- readRDS(file='../Lesson_6/run2ranparcov.RDS')
run2ranparcov

## [[1]]

Run3WTonV

Model Fit Summary

run3WTonVfit<-readRDS("../Lesson_6/run3WTonVfit.RDS")
print(run3WTonVfit)

## $Overall
##    Scenario RetCode    LogLik  -2LL   AIC      BIC nParm nObs nSub EpsShrinkage
## 1: WorkFlow       1 -6744.001 13488 13504 13547.27     8 1650  150      0.12163
##    Condition
## 1:  86.59649
## 
## $theta
##    #Scenario Parameter    Estimate Units      Stderr       CV%      2.5%CI
## 1:  WorkFlow      tvKa  1.17641505    NA 0.034686594  2.948500  1.10838043
## 2:  WorkFlow       tvV 81.39155371    NA 2.937346436  3.608908 75.63021369
## 3:  WorkFlow      tvCl  7.93844179    NA 0.416274785  5.243785  7.12195636
## 4:  WorkFlow     dVdWT  0.09994762    NA 0.093570298 93.619333 -0.08358207
## 5:  WorkFlow      CEps  0.20651568    NA 0.009874888  4.781665  0.18714698
##       97.5%CI Var.Inf.factor
## 1:  1.2444497             NA
## 2: 87.1528937             NA
## 3:  8.7549272             NA
## 4:  0.2834773             NA
## 5:  0.2258844             NA
## 
## $omega
##    Label        nKa        nV       nCl
## 1:   nKa 0.08045648 0.0000000 0.0000000
## 2:    nV 0.00000000 0.1787547 0.0000000
## 3:   nCl 0.00000000 0.0000000 0.3701806
## 
## $omega_Correlation
##    Label nKa nV nCl
## 1:   nKa   1  0   0
## 2:    nV   0  1   0
## 3:   nCl   0  0   1
## 
## $Eta_Shrinkage
##        Label       nKa         nV        nCl
## 1: Shrinkage 0.2166662 0.03497187 0.02198842
## 
## $omega_stderr
##    Label        nKa         nV        nCl
## 1:   nKa 0.01552308 0.00000000 0.00000000
## 2:    nV 0.00000000 0.02328304 0.00000000
## 3:   nCl 0.00000000 0.00000000 0.03884049

Structural Model

DV vs PRED

run3dvpred <- readRDS(file='../Lesson_6/run3dvpred.RDS')
run3dvpred

DV vs IPRED

run3dvipred <- readRDS(file='../Lesson_6/run3dvipred.RDS')
run3dvipred

CWRES vs TIME

run3residv <- readRDS(file='../Lesson_6/run3residv.RDS')
run3residv

CWRES vs IPRED

run3respred <- readRDS(file='../Lesson_6/run3respred.RDS')
run3respred

Covariate Model

ETAs vs All Covariates

run3ranparcov <- readRDS(file='../Lesson_6/run3ranparcov.RDS')
run3ranparcov

## [[1]]

Session Info

sessionInfo()

R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Certara.RsNLME_1.1.0 plotly_4.10.0        ggplot2_3.3.6       
[4] flextable_0.7.3     

loaded via a namespace (and not attached):
 [1] nlme_3.1-157             RColorBrewer_1.1-3       httr_1.4.4              
 [4] tools_4.2.1              backports_1.4.1          bslib_0.4.0             
 [7] utf8_1.2.2               R6_2.5.1                 DBI_1.1.3               
[10] lazyeval_0.2.2           mgcv_1.8-40              colorspace_2.0-3        
[13] withr_2.5.0              gridExtra_2.3            GGally_2.1.2            
[16] tidyselect_1.1.2         curl_4.3.2               compiler_4.2.1          
[19] Certara.Xpose.NLME_1.1.0 cli_3.3.0                gt_0.6.0                
[22] xml2_1.3.3               shinyjs_2.1.0            officer_0.4.3           
[25] shinymaterial_1.2.0      labeling_0.4.2           sass_0.4.2              
[28] scales_1.2.1             checkmate_2.1.0          commonmark_1.8.0        
[31] systemfonts_1.0.4        stringr_1.4.1            digest_0.6.29           
[34] rmarkdown_2.16           katex_1.4.0              base64enc_0.1-3         
[37] Certara.NLME8_1.2.0      pkgconfig_2.0.3          htmltools_0.5.3         
[40] fastmap_1.1.0            highr_0.9                htmlwidgets_1.5.4       
[43] rlang_1.0.4              rstudioapi_0.13          shiny_1.7.2             
[46] jquerylib_0.1.4          farver_2.1.1             generics_0.1.3          
[49] jsonlite_1.8.0           crosstalk_1.2.0          dplyr_1.0.9             
[52] zip_2.2.0                magrittr_2.0.3           Matrix_1.4-1            
[55] Rcpp_1.0.9               munsell_0.5.0            fansi_1.0.3             
[58] gdtools_0.2.4            lifecycle_1.0.1          stringi_1.7.8           
[61] yaml_2.3.5               gtsummary_1.6.1          MASS_7.3-57             
[64] plyr_1.8.7               grid_4.2.1               promises_1.2.0.1        
[67] crayon_1.5.1             egg_0.4.5                lattice_0.20-45         
[70] splines_4.2.1            knitr_1.40               pillar_1.8.1            
[73] uuid_1.1-0               xslt_1.4.3               glue_1.6.2              
[76] evaluate_0.16            V8_4.2.1                 data.table_1.14.2       
[79] broom.helpers_1.8.0      vctrs_0.4.1              tweenr_1.0.2            
[82] httpuv_1.6.5             gtable_0.3.0             purrr_0.3.4             
[85] polyclip_1.10-0          tidyr_1.2.0              reshape_0.8.9           
[88] xpose_0.4.13             assertthat_0.2.1         cachem_1.0.6            
[91] xfun_0.32                ggforce_0.3.3            mime_0.12               
[94] xtable_1.8-4             later_1.3.0              equatags_0.2.0          
[97] viridisLite_0.4.1        tibble_3.1.8             ellipsis_0.3.2

	Dose Group
Time (hr)	5000 μg	10000 μg	20000 μg
0.00	0 (0-0)	0 (0-0)	0 (0-0)
0.25	19.5 (5.19-56.4)	38.8 (4.77-111)	58.3 (6.83-164)
0.50	32.6 (9.88-84.3)	64.9 (19-167)	96.9 (27.9-247)
1.00	48.1 (17.2-119)	96.3 (30.6-235)	144 (41.1-349)
2.00	56.2 (17.7-116)	112 (32.7-236)	169 (51.7-352)
3.00	55.8 (19.8-118)	112 (37.7-235)	167 (56-348)
4.00	51.7 (15.4-131)	103 (27.5-264)	155 (37.8-400)
6.00	41.2 (9.77-91.4)	82.4 (14.3-182)	124 (21.9-270)
8.00	34.7 (9.36-67)	69.9 (21.7-136)	104 (29.8-199)
12.00	25 (2.98-69.8)	49.6 (5.06-140)	75.8 (8.52-208)
16.00	18.7 (1.08-57.5)	37.5 (0-115)	56.8 (0-169)
24.00	9.52 (0.103-32.9)	19.3 (0-67.3)	28.1 (0-99.8)

Data Memo Example

Keith Nieforth & James Craig

September 20, 2022

Notes and Conventions

Notes

Conventions used in this document

Objectives

Study Design

Methods

Overview of the data

Summary

Demography summary

Mean Concentration Summary

PK profiles Linear

PK profiles Log

Facet Gender and Dose Linear

Facet Race and Dose Linear

Facet WT Linear

Facet Age Linear

Base Model Development

Summary

Model Description

Model Fit Summary

Additive Error Model

Multiplicative Error Model

Base Model Diagnostics

Additive Residual Error Model

Structural Model

DV vs PRED

DV vs IPRED

CWRES vs TIME

CWRES vs IPRED

absCWRES vs IPRED

Covariate Model

Parameter vs WT

ETA vs WT

Multiplicative Residual Error Model

Structural Model

DV vs PRED

DV vs IPRED

CWRES vs TIME

CWRES vs IPRED

absCWRES vs IPRED

Covariate Model

Parameter vs WT

ETA vs WT

ETAs vs All Covariates

Covariate Models

Summary

Run1WTonCL

Model Fit Summary

Structural Model

DV vs PRED

DV vs IPRED

CWRES vs TIME

CWRES vs IPRED

Covariate Model

ETAs vs All Covariates

Run2SEXonCL

Model Fit Summary

Structural Model

DV vs PRED

DV vs IPRED

CWRES vs TIME

CWRES vs IPRED

Covariate Model

ETAs vs All Covariates

Run3WTonV

Model Fit Summary

Structural Model

DV vs PRED

DV vs IPRED

CWRES vs TIME

CWRES vs IPRED

Covariate Model

ETAs vs All Covariates

Session Info